GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters

Abstract

We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy's bulge-to-total light ratio ($L_B/L_T$), effective radius ($R_e$), and flux ($F$). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a Spatial Transformer Network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to match $z<0.25$ galaxies in Hyper Suprime-Cam Wide $g$-band images, we demonstrate that GaMPEN achieves typical errors of $0.1$ in $L_B/L_T$, $0.17$ arcsec ($\sim 7\%$) in $R_e$, and $6.3\times 10^4$ nJy ($\sim 1\%$) in $F$. GaMPEN's predicted uncertainties are well-calibrated and accurate ($<5\%$ deviation) -- for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We also demonstrate that we can apply categorical labels (i.e., classifications such as "highly bulge-dominated") to predictions in regions with high residuals and verify that those labels are $\gtrsim 97\%$ accurate. To the best of our knowledge, GaMPEN is the first machine learning framework for determining joint posterior distributions of multiple morphological parameters and is also the first application of an STN to optical imaging in astronomy.

arXiv commentAccepted for publication in The Astrophysical Journal. We welcome comments and constructive criticism. Digital assets will be available at http://gampen.ghosharitra.com

References in corpus (43)

• Array Programming with NumPy
• Deep Learning in Neural Networks: An Overview
• Astropy: A Community Python Package for Astronomy
• The Sloan Digital Sky Survey: Technical Summary
• Binary companions of evolved stars in APOGEE DR14: Search method and catalog of ~5,000 companions
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• Detailed Structural Decomposition of Galaxy Images
• The slope of the black-hole mass versus velocity dispersion correlation
• CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey
• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• 3D-HST+CANDELS: The Evolution of the Galaxy Size-Mass Distribution since $z=3$
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Galaxy Structure and Mode of Star Formation in the SFR-Mass Plane from z ~ 2.5 to z ~ 0.1
• The Green Valley is a Red Herring: Galaxy Zoo reveals two evolutionary pathways towards quenching of star formation in early- and late-type galaxies
• First Data Release of the Hyper Suprime-Cam Subaru Strategic Program
• Second Data Release of the Hyper Suprime-Cam Subaru Strategic Program
• zCOSMOS - 10k-bright spectroscopic sample. The bimodality in the Galaxy Stellar Mass Function: exploring its evolution with redshift
• The DEEP Groth Strip Survey II. Hubble Space Telescope Structural Parameters of Galaxies in the Groth Strip
• GEMS: Galaxy Evolution from Morphologies and SEDs
• Identifying Exoplanets with Deep Learning: A Five Planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90
• The massive end of the luminosity and stellar mass functions: Dependence on the fit to the light profile
• GEMS: Galaxy fitting catalogues and testing parametric galaxy fitting codes
• A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
• Star-galaxy Classification Using Deep Convolutional Neural Networks
• Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
• Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Galaxy Zoo: Quantitative Visual Morphological Classifications for 48,000 galaxies from CANDELS
• Mass assembly and morphological transformations since $z\sim 3$ from CANDELS
• Radio Galaxy Zoo: ClaRAN - A Deep Learning Classifier for Radio Morphologies
• The Euclid mission design
• Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data
• Deep learning for galaxy surface brightness profile fitting
• Application of Convolutional Neural Networks for Stellar Spectral Classification
• Simulations of single and two-component galaxy decompositions for spectroscopically selected galaxies from the Sloan Digital Sky Survey
• The relationship between fine galaxy stellar morphology and star formation activity in cosmological simulations: a deep learning view
• Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
• Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in $\sim 100,000$ SDSS and $\sim 20,000$ CANDELS Galaxies
• Coincidence between morphology and star-formation activity through cosmic time: the impact of the bulge growth
• Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
• Pushing automated morphological classifications to their limits with the Dark Energy Survey
• Morphology and the Color-Mass Diagram as Clues to Galaxy Evolution at z~1
• Subaru Hyper Suprime-Cam revisits the large-scale environmental dependence on galaxy morphology over 360 deg${}^2$ at z=0.3-0.6

Cited by in corpus (7)

• From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation
• Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey
• Denser Environments Cultivate Larger Galaxies: A Comprehensive Study beyond the Local Universe with 3 Million Hyper Suprime-Cam Galaxies
• How to set up your first machine learning project in astronomy
• Using Machine Learning to Determine Morphologies of $z<1$ AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey
• Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within $z<1.4$ in the Hyper Supreme-Cam Wide Survey
• Environmental Sculpting of Galaxy Structure at Fixed Stellar Mass: A Multi-Scale Analysis Across Cosmic Time using 3 Million HSC Galaxies